Models and Measurement in Quantitative Sociology
Undergraduate: Level 6
Thursday 08 October 2020
Friday 02 July 2021
29 June 2020
Requisites for this module
SC203 or GV207 or SC208
BSC L315 Sociology (Applied Quantitative Research),
BSC L316 Sociology (Applied Quantitative Research) (Including Year Abroad),
BSC L317 Sociology (Applied Quantitative Research) (Including Placement Year),
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (including foundation Year)
The first term of the module begins with simple OLS regression and provides a framework for modelling strategy and variable selection. Students are then taken through extensions to the basic OLS model, with categorical predictors, interactions and non-linear terms. Next, we introduce models for categorical outcomes: binary logistic and multinomial logit. The term concludes with a discussion of practical topics in data analysis - how to deal with complex sample designs, weighting and non-response adjustments.
The second part of the module introduces students to principles of measurement and provides statistical methods for realising empirical measurement models. The first lectures cover basic classical test theory, concepts of reliability and validity, and demonstrate simple methods for developing scales and indexes to measure sociological phenomena. Latent variable models are introduced in the form of exploratory factor analysis, and then the focus switches to confirmatory factor analysis models. At this point students are introduced to the SPSS AMOS structural equation modelling software. The module concludes by integrating general linear models with measurement models in the form of full structural equation modelling. This brings together in one statistical framework the principles and techniques learned throughout the year.
This module will develop students' understanding of quantitative analysis and impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software.
By the end of course students should be able to:
understand the principles and practice of statistical modelling
critically evaluate research articles that use statistics
understand the link between substantive theory, measurement and statistical models
carry out intermediate and advanced statistical analysis using SPSS and AMOSS
If you wish to take this module but have not taken the second year module 'Researching Social Life II' (SC203-5-FY), please contact the module supervisor to see if you have the appropriate background in statistics.
Please note that assessment information is currently showing for 2019-20 and will be updated in September.
No information available.
This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
||Class test 1
||Class Test 2
||Data Analysis Exercise 1
||Data Analysis Exercise 2
Module supervisor and teaching staff
Prof Nick Allum, email: firstname.lastname@example.org.
Professor Nick Allum
Jane Harper, Undergraduate Administrator, Telephone: 01206 873052
No external examiner information available for this module.
Available via Moodle
Of 61 hours, 20 (32.8%) hours available to students:
41 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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